Detecting Cumulated Anomaly by a Dubiety Degree based detection Model

Author

Lu, Gang ; Yi, Junkai ; Lü, Kevin

Author_Institution

Beijing Univ. of Chem. Technol., Beijing

Volume

3

fYear

2007

fDate

July 30 2007-Aug. 1 2007

Firstpage

1034

Lastpage

1039

Abstract

The concept of cumulated anomaly is addressed in this paper, which describes a new type of database anomalies. A detection model, dubiety-determining model (DDM), for cumulated anomaly, is proposed. This model is based on statistical theories and fuzzy set theories. The DDM can measure the dubiety degree of each database transaction quantitatively. We designed software system architecture to support the DDM for monitoring database transactions. We also implemented the system and tested it. Our experimental results show that the DDM method is feasible and effective.

Keywords

database management systems; fuzzy set theory; security of data; software architecture; statistical analysis; cumulated anomaly detection; database anomalies; database transaction monitoring; dubiety degree based detection model; dubiety-determining model; fuzzy set theories; software system architecture; statistical theories; Chemical technology; Computer architecture; Distributed decision making; Fuzzy set theory; Intrusion detection; Monitoring; Software design; Software engineering; Software systems; Transaction databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on

Conference_Location

Qingdao

Print_ISBN

978-0-7695-2909-7

Type

conf

DOI

10.1109/SNPD.2007.187

Filename

4288001